source("../../lib/som-utils.R")
Attaching package: 'dplyr'
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
source("../../lib/maps-utils.R")
Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1
mpr.set_base_path_analysis()
model <- mpr.load_model("som-289.rds.xz")
summary(model)
SOM of size 5x5 with a hexagonal topology and a bubble neighbourhood function.
The number of data layers is 1.
Distance measure(s) used: sumofsquares.
Training data included: 94881 objects.
Mean distance to the closest unit in the map: 1.172.
plot(model, type="changes")
df <- mpr.load_data("datos_mes.csv.xz")
df
summary(df)
id_estacion fecha fecha_cnt tmax
Length:94881 Length:94881 Min. : 1.000 Min. :-53.0
Class :character Class :character 1st Qu.: 4.000 1st Qu.:148.0
Mode :character Mode :character Median : 6.000 Median :198.0
Mean : 6.497 Mean :200.2
3rd Qu.: 9.000 3rd Qu.:255.0
Max. :12.000 Max. :403.0
tmin precip nevada prof_nieve
Min. :-121.00 Min. : 0.00 Min. :0.000000 Min. : 0.000
1st Qu.: 53.00 1st Qu.: 3.00 1st Qu.:0.000000 1st Qu.: 0.000
Median : 98.00 Median : 10.00 Median :0.000000 Median : 0.000
Mean : 98.86 Mean : 16.25 Mean :0.000295 Mean : 0.467
3rd Qu.: 148.00 3rd Qu.: 22.00 3rd Qu.:0.000000 3rd Qu.: 0.000
Max. : 254.00 Max. :422.00 Max. :6.000000 Max. :1834.000
longitud latitud altitud
Min. :27.82 Min. :-17.8889 Min. : 1.0
1st Qu.:38.28 1st Qu.: -5.6417 1st Qu.: 42.0
Median :40.82 Median : -3.4500 Median : 247.0
Mean :39.66 Mean : -3.4350 Mean : 418.5
3rd Qu.:42.08 3rd Qu.: 0.4914 3rd Qu.: 656.0
Max. :43.57 Max. : 4.2156 Max. :2535.0
world <- ne_countries(scale = "medium", returnclass = "sf")
spain <- subset(world, admin == "Spain")
plot(model, type="count", shape = "straight", palette.name = mpr.degrade.bleu)
NĂºmero de elementos en cada celda:
nb <- table(model$unit.classif)
print(nb)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
5079 6817 5120 2530 630 5674 2239 3219 2480 2846 5115 4799 4408 3598 2 6316
17 18 19 20 21 22 23 24 25
5326 6861 7044 10 6047 7460 64 8 1189
ComprobaciĂ³n de nodos vacĂos:
dim_model <- 5*5;
len_nb = length(nb);
empty_nodes <- dim_model != len_nb;
if (empty_nodes) {
print(paste("[Warning] Existen nodos vacĂos: ", len_nb, "/", dim_model))
}
plot(model, type="dist.neighbours", shape = "straight")
model_colnames = c("tmax", "tmin", "precip", "nevada", "prof_nieve", "longitud", "latitud", "altitud")
model_ncol = length(model_colnames)
plot(model, shape = "straight")
par(mfrow=c(3,4))
for (j in 1:model_ncol) {
plot(model, type="property", property=getCodes(model,1)[,j],
palette.name=mpr.coolBlueHotRed,
main=model_colnames[j],
cex=0.5, shape = "straight")
}
if (!empty_nodes) {
cor <- apply(getCodes(model,1), 2, mpr.weighted.correlation, w=nb, som=model)
print(cor)
}
tmax tmin precip nevada prof_nieve longitud
[1,] -0.7648887 -0.7582582 0.6200670 0.020915288 0.03149500 0.4479989
[2,] 0.3432055 0.1640591 -0.2969694 0.004657616 0.03215826 0.1393199
latitud altitud
[1,] 0.14844188 0.4728733
[2,] -0.01755875 0.3996106
RepresentaciĂ³n de cada variable en un mapa de factores:
if (!empty_nodes) {
par(mfrow=c(1,1))
plot(cor[1,], cor[2,], xlim=c(-1,1), ylim=c(-1,1), type="n")
lines(c(-1,1),c(0,0))
lines(c(0,0),c(-1,1))
text(cor[1,], cor[2,], labels=model_colnames, cex=0.75)
symbols(0,0,circles=1,inches=F,add=T)
}
Importancia de cada variable - varianza ponderada por el tamaño de la celda:
if (!empty_nodes) {
sigma2 <- sqrt(apply(getCodes(model,1),2,function(x,effectif)
{m<-sum(effectif*(x-weighted.mean(x,effectif))^2)/(sum(effectif)-1)},
effectif=nb))
print(sort(sigma2,decreasing=T))
}
longitud nevada altitud latitud tmax tmin prof_nieve
0.9495866 0.9409413 0.9277203 0.9196942 0.9179936 0.9136398 0.9131914
precip
0.8689148
if (!empty_nodes) {
hac <- mpr.hac(model, nb)
}
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=3)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=3)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 4.000 1st Qu.:148.0 1st Qu.: 53.00 1st Qu.: 3.00
Median : 6.000 Median :198.0 Median : 98.00 Median : 10.00
Mean : 6.497 Mean :200.2 Mean : 98.87 Mean : 16.25
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.00 3rd Qu.: 22.00
Max. :12.000 Max. :403.0 Max. : 254.00 Max. :422.00
nevada prof_nieve longitud latitud
Min. :0.0000000 Min. : 0.0000 Min. :27.82 Min. :-17.8889
1st Qu.:0.0000000 1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417
Median :0.0000000 Median : 0.0000 Median :40.82 Median : -3.4500
Mean :0.0001897 Mean : 0.3714 Mean :39.66 Mean : -3.4350
3rd Qu.:0.0000000 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914
Max. :3.0000000 Max. :709.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 247.0
Mean : 418.4
3rd Qu.: 656.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-4.00 Min. :-51.00 Min. : 19.00 Min. :0
1st Qu.:2.000 1st Qu.: 4.50 1st Qu.:-43.50 1st Qu.: 45.00 1st Qu.:0
Median :2.500 Median :21.00 Median :-30.00 Median : 50.00 Median :0
Mean :2.375 Mean :18.88 Mean :-33.00 Mean : 58.25 Mean :0
3rd Qu.:3.000 3rd Qu.:35.25 3rd Qu.:-23.75 3rd Qu.: 63.75 3rd Qu.:0
Max. :3.000 Max. :36.00 Max. :-16.00 Max. :122.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 784.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.: 865.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1045.0 Median :40.78 Median :-4.01 Median :1894
Mean :1131.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1249.5 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=4)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=4)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 4.000 1st Qu.:148.0 1st Qu.: 53.00 1st Qu.: 3.00
Median : 6.000 Median :198.0 Median : 98.00 Median : 10.00
Mean : 6.497 Mean :200.2 Mean : 98.88 Mean : 16.25
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.00 3rd Qu.: 22.00
Max. :12.000 Max. :403.0 Max. : 254.00 Max. :422.00
nevada prof_nieve longitud latitud
Min. :0 Min. : 0.0000 Min. :27.82 Min. :-17.8889
1st Qu.:0 1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417
Median :0 Median : 0.0000 Median :40.82 Median : -3.4500
Mean :0 Mean : 0.3714 Mean :39.66 Mean : -3.4351
3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914
Max. :0 Max. :709.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 247.0
Mean : 418.4
3rd Qu.: 656.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip
Min. : 1.0 Min. : 69.00 Min. :-24.00 Min. : 3.00
1st Qu.: 2.0 1st Qu.: 86.25 1st Qu.:-12.50 1st Qu.: 6.00
Median : 7.0 Median : 92.00 Median : 6.50 Median :10.50
Mean : 6.8 Mean : 96.80 Mean : 7.90 Mean :11.40
3rd Qu.:12.0 3rd Qu.: 96.75 3rd Qu.: 29.25 3rd Qu.:14.75
Max. :12.0 Max. :140.00 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud altitud
Min. :1.00 Min. :0.0 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:1.25 1st Qu.:0.0 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:263.0
Median :2.00 Median :0.5 Median :41.08 Median :-2.242 Median :435.6
Mean :1.80 Mean :1.1 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:2.00 3rd Qu.:1.0 3rd Qu.:41.67 3rd Qu.:-1.033 3rd Qu.:608.1
Max. :3.00 Max. :7.0 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-4.00 Min. :-51.00 Min. : 19.00 Min. :0
1st Qu.:2.000 1st Qu.: 4.50 1st Qu.:-43.50 1st Qu.: 45.00 1st Qu.:0
Median :2.500 Median :21.00 Median :-30.00 Median : 50.00 Median :0
Mean :2.375 Mean :18.88 Mean :-33.00 Mean : 58.25 Mean :0
3rd Qu.:3.000 3rd Qu.:35.25 3rd Qu.:-23.75 3rd Qu.: 63.75 3rd Qu.:0
Max. :3.000 Max. :36.00 Max. :-16.00 Max. :122.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 784.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.: 865.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1045.0 Median :40.78 Median :-4.01 Median :1894
Mean :1131.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1249.5 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=5)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=5)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 4.000 1st Qu.:149.0 1st Qu.: 53.00 1st Qu.: 3.00
Median : 6.000 Median :198.0 Median : 98.00 Median : 10.00
Mean : 6.499 Mean :200.3 Mean : 98.96 Mean : 16.22
3rd Qu.: 9.000 3rd Qu.:255.0 3rd Qu.: 148.00 3rd Qu.: 22.00
Max. :12.000 Max. :403.0 Max. : 254.00 Max. :422.00
nevada prof_nieve longitud latitud
Min. :0 Min. : 0.0000 Min. :27.82 Min. :-17.8889
1st Qu.:0 1st Qu.: 0.0000 1st Qu.:38.28 1st Qu.: -5.6417
Median :0 Median : 0.0000 Median :40.82 Median : -3.4500
Mean :0 Mean : 0.1427 Mean :39.66 Mean : -3.4347
3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:42.08 3rd Qu.: 0.4914
Max. :0 Max. :165.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 42.0
Median : 247.0
Mean : 417.4
3rd Qu.: 656.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip
Min. : 1.0 Min. : 69.00 Min. :-24.00 Min. : 3.00
1st Qu.: 2.0 1st Qu.: 86.25 1st Qu.:-12.50 1st Qu.: 6.00
Median : 7.0 Median : 92.00 Median : 6.50 Median :10.50
Mean : 6.8 Mean : 96.80 Mean : 7.90 Mean :11.40
3rd Qu.:12.0 3rd Qu.: 96.75 3rd Qu.: 29.25 3rd Qu.:14.75
Max. :12.0 Max. :140.00 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud altitud
Min. :1.00 Min. :0.0 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:1.25 1st Qu.:0.0 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:263.0
Median :2.00 Median :0.5 Median :41.08 Median :-2.242 Median :435.6
Mean :1.80 Mean :1.1 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:2.00 3rd Qu.:1.0 3rd Qu.:41.67 3rd Qu.:-1.033 3rd Qu.:608.1
Max. :3.00 Max. :7.0 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-12.00 Min. :-72.00 Min. : 5.00
1st Qu.: 1.000 1st Qu.: 12.75 1st Qu.:-40.75 1st Qu.: 30.75
Median : 2.000 Median : 27.00 Median :-26.50 Median : 49.50
Mean : 3.062 Mean : 30.47 Mean :-26.97 Mean : 50.75
3rd Qu.: 3.000 3rd Qu.: 46.00 3rd Qu.:-16.50 3rd Qu.: 68.50
Max. :12.000 Max. : 99.00 Max. : 28.00 Max. :180.00
nevada prof_nieve longitud latitud altitud
Min. :0 Min. :175.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:0 1st Qu.:218.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :0 Median :280.5 Median :40.78 Median :-4.01 Median :1894
Mean :0 Mean :339.0 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:0 3rd Qu.:438.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :0 Max. :709.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-4.00 Min. :-51.00 Min. : 19.00 Min. :0
1st Qu.:2.000 1st Qu.: 4.50 1st Qu.:-43.50 1st Qu.: 45.00 1st Qu.:0
Median :2.500 Median :21.00 Median :-30.00 Median : 50.00 Median :0
Mean :2.375 Mean :18.88 Mean :-33.00 Mean : 58.25 Mean :0
3rd Qu.:3.000 3rd Qu.:35.25 3rd Qu.:-23.75 3rd Qu.: 63.75 3rd Qu.:0
Max. :3.000 Max. :36.00 Max. :-16.00 Max. :122.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 784.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.: 865.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1045.0 Median :40.78 Median :-4.01 Median :1894
Mean :1131.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1249.5 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=6)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=6)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
df.cluster06 <- subset(df, cluster==6)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :-53 Min. :-121.00 Min. : 0.00 Min. :0
1st Qu.: 4.000 1st Qu.:146 1st Qu.: 50.00 1st Qu.: 3.00 1st Qu.:0
Median : 6.000 Median :194 Median : 93.00 Median : 11.00 Median :0
Mean : 6.499 Mean :199 Mean : 94.93 Mean : 16.95 Mean :0
3rd Qu.: 9.000 3rd Qu.:255 3rd Qu.: 141.00 3rd Qu.: 23.00 3rd Qu.:0
Max. :12.000 Max. :403 Max. : 254.00 Max. :422.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.0000 Min. :27.82 Min. :-17.8889 Min. : 1.0
1st Qu.: 0.0000 1st Qu.:38.99 1st Qu.: -4.8500 1st Qu.: 44.0
Median : 0.0000 Median :40.96 Median : -2.4831 Median : 251.0
Mean : 0.1514 Mean :40.54 Mean : -2.4502 Mean : 409.5
3rd Qu.: 0.0000 3rd Qu.:42.23 3rd Qu.: 0.4942 3rd Qu.: 667.0
Max. :165.0000 Max. :43.57 Max. : 4.2156 Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : 13.0 Min. :-33.0 Min. : 0.00 Min. :0
1st Qu.: 4.000 1st Qu.:201.0 1st Qu.:132.0 1st Qu.: 0.00 1st Qu.:0
Median : 7.000 Median :225.0 Median :160.0 Median : 1.00 Median :0
Mean : 6.503 Mean :217.5 Mean :150.7 Mean : 6.86 Mean :0
3rd Qu.: 9.000 3rd Qu.:253.0 3rd Qu.:190.0 3rd Qu.: 8.00 3rd Qu.:0
Max. :12.000 Max. :356.0 Max. :244.0 Max. :114.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.00000 Min. :27.82 Min. :-17.89 Min. : 14.0
1st Qu.: 0.00000 1st Qu.:28.31 1st Qu.:-16.56 1st Qu.: 25.0
Median : 0.00000 Median :28.44 Median :-16.33 Median : 35.0
Mean : 0.03089 Mean :28.36 Mean :-16.05 Mean : 518.5
3rd Qu.: 0.00000 3rd Qu.:28.48 3rd Qu.:-15.39 3rd Qu.: 632.0
Max. :46.00000 Max. :28.95 Max. :-13.60 Max. :2371.0
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip
Min. : 1.0 Min. : 69.00 Min. :-24.00 Min. : 3.00
1st Qu.: 2.0 1st Qu.: 86.25 1st Qu.:-12.50 1st Qu.: 6.00
Median : 7.0 Median : 92.00 Median : 6.50 Median :10.50
Mean : 6.8 Mean : 96.80 Mean : 7.90 Mean :11.40
3rd Qu.:12.0 3rd Qu.: 96.75 3rd Qu.: 29.25 3rd Qu.:14.75
Max. :12.0 Max. :140.00 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud altitud
Min. :1.00 Min. :0.0 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:1.25 1st Qu.:0.0 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:263.0
Median :2.00 Median :0.5 Median :41.08 Median :-2.242 Median :435.6
Mean :1.80 Mean :1.1 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:2.00 3rd Qu.:1.0 3rd Qu.:41.67 3rd Qu.:-1.033 3rd Qu.:608.1
Max. :3.00 Max. :7.0 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-12.00 Min. :-72.00 Min. : 5.00
1st Qu.: 1.000 1st Qu.: 12.75 1st Qu.:-40.75 1st Qu.: 30.75
Median : 2.000 Median : 27.00 Median :-26.50 Median : 49.50
Mean : 3.062 Mean : 30.47 Mean :-26.97 Mean : 50.75
3rd Qu.: 3.000 3rd Qu.: 46.00 3rd Qu.:-16.50 3rd Qu.: 68.50
Max. :12.000 Max. : 99.00 Max. : 28.00 Max. :180.00
nevada prof_nieve longitud latitud altitud
Min. :0 Min. :175.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:0 1st Qu.:218.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :0 Median :280.5 Median :40.78 Median :-4.01 Median :1894
Mean :0 Mean :339.0 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:0 3rd Qu.:438.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :0 Max. :709.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster06)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-4.00 Min. :-51.00 Min. : 19.00 Min. :0
1st Qu.:2.000 1st Qu.: 4.50 1st Qu.:-43.50 1st Qu.: 45.00 1st Qu.:0
Median :2.500 Median :21.00 Median :-30.00 Median : 50.00 Median :0
Mean :2.375 Mean :18.88 Mean :-33.00 Mean : 58.25 Mean :0
3rd Qu.:3.000 3rd Qu.:35.25 3rd Qu.:-23.75 3rd Qu.: 63.75 3rd Qu.:0
Max. :3.000 Max. :36.00 Max. :-16.00 Max. :122.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 784.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.: 865.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1045.0 Median :40.78 Median :-4.01 Median :1894
Mean :1131.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1249.5 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.hist(df.cluster06)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster06)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=8)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=8)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
df.cluster06 <- subset(df, cluster==6)
df.cluster07 <- subset(df, cluster==7)
df.cluster08 <- subset(df, cluster==8)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.0 Min. :-121.00 Min. : 0.00
1st Qu.: 2.000 1st Qu.:127.0 1st Qu.: 32.00 1st Qu.: 6.00
Median : 5.000 Median :161.0 Median : 64.00 Median : 16.00
Mean : 6.031 Mean :159.4 Mean : 65.05 Mean : 20.81
3rd Qu.:10.000 3rd Qu.:192.0 3rd Qu.: 96.00 3rd Qu.: 30.00
Max. :12.000 Max. :352.0 Max. : 221.00 Max. :126.00
nevada prof_nieve longitud latitud
Min. :0 Min. : 0.0000 Min. :28.48 Min. :-16.3292
1st Qu.:0 1st Qu.: 0.0000 1st Qu.:39.56 1st Qu.: -5.6000
Median :0 Median : 0.0000 Median :41.42 Median : -2.9553
Mean :0 Mean : 0.2245 Mean :40.89 Mean : -2.6932
3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:42.52 3rd Qu.: 0.4914
Max. :0 Max. :165.0000 Max. :43.57 Max. : 4.2156
altitud
Min. : 1.0
1st Qu.: 47.0
Median : 251.0
Mean : 425.9
3rd Qu.: 667.0
Max. :2535.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : -8.0 Min. :-58.00 Min. :100.0 Min. :0
1st Qu.: 3.000 1st Qu.:119.0 1st Qu.: 57.00 1st Qu.:110.0 1st Qu.:0
Median :10.000 Median :140.0 Median : 78.00 Median :121.0 Median :0
Mean : 7.533 Mean :146.3 Mean : 79.49 Mean :132.5 Mean :0
3rd Qu.:11.000 3rd Qu.:170.0 3rd Qu.:102.00 3rd Qu.:141.8 3rd Qu.:0
Max. :12.000 Max. :350.0 Max. :223.00 Max. :422.0 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.0000 Min. :27.82 Min. :-17.889 Min. : 1.00
1st Qu.: 0.0000 1st Qu.:41.29 1st Qu.: -8.616 1st Qu.: 36.75
Median : 0.0000 Median :42.43 Median : -7.860 Median : 256.00
Mean : 0.6936 Mean :41.34 Mean : -5.770 Mean : 340.98
3rd Qu.: 0.0000 3rd Qu.:42.89 3rd Qu.: -2.039 3rd Qu.: 370.00
Max. :123.0000 Max. :43.57 Max. : 3.035 Max. :2400.00
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : 13.0 Min. :-33.0 Min. : 0.00 Min. :0
1st Qu.: 4.000 1st Qu.:201.0 1st Qu.:132.0 1st Qu.: 0.00 1st Qu.:0
Median : 7.000 Median :225.0 Median :160.0 Median : 1.00 Median :0
Mean : 6.503 Mean :217.5 Mean :150.7 Mean : 6.86 Mean :0
3rd Qu.: 9.000 3rd Qu.:253.0 3rd Qu.:190.0 3rd Qu.: 8.00 3rd Qu.:0
Max. :12.000 Max. :356.0 Max. :244.0 Max. :114.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.00000 Min. :27.82 Min. :-17.89 Min. : 14.0
1st Qu.: 0.00000 1st Qu.:28.31 1st Qu.:-16.56 1st Qu.: 25.0
Median : 0.00000 Median :28.44 Median :-16.33 Median : 35.0
Mean : 0.03089 Mean :28.36 Mean :-16.05 Mean : 518.5
3rd Qu.: 0.00000 3rd Qu.:28.48 3rd Qu.:-15.39 3rd Qu.: 632.0
Max. :46.00000 Max. :28.95 Max. :-13.60 Max. :2371.0
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :163.0 Min. : 42.0 Min. : 0.000 Min. :0
1st Qu.: 6.000 1st Qu.:245.0 1st Qu.:122.0 1st Qu.: 1.000 1st Qu.:0
Median : 7.000 Median :275.0 Median :152.0 Median : 5.000 Median :0
Mean : 7.361 Mean :274.8 Mean :151.6 Mean : 7.276 Mean :0
3rd Qu.: 9.000 3rd Qu.:303.0 3rd Qu.:181.0 3rd Qu.:12.000 3rd Qu.:0
Max. :12.000 Max. :403.0 Max. :254.0 Max. :44.000 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.00000 Min. :35.28 Min. :-8.6239 Min. : 1.0
1st Qu.: 0.00000 1st Qu.:38.28 1st Qu.:-4.1153 1st Qu.: 43.0
Median : 0.00000 Median :40.41 Median :-1.8631 Median : 247.0
Mean : 0.00245 Mean :39.88 Mean :-1.9231 Mean : 380.1
3rd Qu.: 0.00000 3rd Qu.:41.52 3rd Qu.: 0.5706 3rd Qu.: 667.0
Max. :35.00000 Max. :43.36 Max. : 4.2156 Max. :1894.0
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster06)
fecha_cnt tmax tmin precip
Min. : 1.0 Min. : 69.00 Min. :-24.00 Min. : 3.00
1st Qu.: 2.0 1st Qu.: 86.25 1st Qu.:-12.50 1st Qu.: 6.00
Median : 7.0 Median : 92.00 Median : 6.50 Median :10.50
Mean : 6.8 Mean : 96.80 Mean : 7.90 Mean :11.40
3rd Qu.:12.0 3rd Qu.: 96.75 3rd Qu.: 29.25 3rd Qu.:14.75
Max. :12.0 Max. :140.00 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud altitud
Min. :1.00 Min. :0.0 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:1.25 1st Qu.:0.0 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:263.0
Median :2.00 Median :0.5 Median :41.08 Median :-2.242 Median :435.6
Mean :1.80 Mean :1.1 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:2.00 3rd Qu.:1.0 3rd Qu.:41.67 3rd Qu.:-1.033 3rd Qu.:608.1
Max. :3.00 Max. :7.0 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster07)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-12.00 Min. :-72.00 Min. : 5.00
1st Qu.: 1.000 1st Qu.: 12.75 1st Qu.:-40.75 1st Qu.: 30.75
Median : 2.000 Median : 27.00 Median :-26.50 Median : 49.50
Mean : 3.062 Mean : 30.47 Mean :-26.97 Mean : 50.75
3rd Qu.: 3.000 3rd Qu.: 46.00 3rd Qu.:-16.50 3rd Qu.: 68.50
Max. :12.000 Max. : 99.00 Max. : 28.00 Max. :180.00
nevada prof_nieve longitud latitud altitud
Min. :0 Min. :175.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:0 1st Qu.:218.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :0 Median :280.5 Median :40.78 Median :-4.01 Median :1894
Mean :0 Mean :339.0 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:0 3rd Qu.:438.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :0 Max. :709.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster08)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-4.00 Min. :-51.00 Min. : 19.00 Min. :0
1st Qu.:2.000 1st Qu.: 4.50 1st Qu.:-43.50 1st Qu.: 45.00 1st Qu.:0
Median :2.500 Median :21.00 Median :-30.00 Median : 50.00 Median :0
Mean :2.375 Mean :18.88 Mean :-33.00 Mean : 58.25 Mean :0
3rd Qu.:3.000 3rd Qu.:35.25 3rd Qu.:-23.75 3rd Qu.: 63.75 3rd Qu.:0
Max. :3.000 Max. :36.00 Max. :-16.00 Max. :122.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 784.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.: 865.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1045.0 Median :40.78 Median :-4.01 Median :1894
Mean :1131.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1249.5 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.hist(df.cluster06)
if (!empty_nodes) mpr.hist(df.cluster07)
if (!empty_nodes) mpr.hist(df.cluster08)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster06)
if (!empty_nodes) mpr.boxplot(df.cluster07)
if (!empty_nodes) mpr.boxplot(df.cluster08)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)
if (!empty_nodes) {
plot(hac, hang=-1, labels=F)
rect.hclust(hac, k=10)
}
A quĂ© clĂºster pertenece cada nodo del mapa de kohonen:
if (!empty_nodes) {
groups <- cutree(hac, k=10)
plot(model, type="mapping",
bgcol=c("steelblue1","sienna1","yellowgreen","red","blue","yellow","purple","green","white","#1f77b4", '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2')[groups],
shape = "straight", labels = "")
add.cluster.boundaries(model, clustering=groups)
}
if (!empty_nodes) {
# Asignamos a cada registro su clĂºster
df$cluster <- groups[model$unit.classif]
}
Nuevos dataframes por cluster
if (!empty_nodes) {
# Creo nuevos dataframes, uno por cada clĂºster.
df.cluster01 <- subset(df, cluster==1)
df.cluster02 <- subset(df, cluster==2)
df.cluster03 <- subset(df, cluster==3)
df.cluster04 <- subset(df, cluster==4)
df.cluster05 <- subset(df, cluster==5)
df.cluster06 <- subset(df, cluster==6)
df.cluster07 <- subset(df, cluster==7)
df.cluster08 <- subset(df, cluster==8)
df.cluster09 <- subset(df, cluster==9)
df.cluster10 <- subset(df, cluster==10)
# Extraigo del dataframe las features.
df.cluster01 <- select(df.cluster01, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster02 <- select(df.cluster02, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster03 <- select(df.cluster03, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster04 <- select(df.cluster04, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster05 <- select(df.cluster05, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster06 <- select(df.cluster06, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster07 <- select(df.cluster07, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster08 <- select(df.cluster08, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster09 <- select(df.cluster09, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
df.cluster10 <- select(df.cluster10, fecha_cnt, tmax, tmin, precip, nevada, prof_nieve, longitud, latitud, altitud)
}
if (!empty_nodes) summary(df.cluster01)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : -4.0 Min. :-110.0 Min. : 0.00 Min. :0
1st Qu.: 2.000 1st Qu.:131.0 1st Qu.: 35.0 1st Qu.: 6.00 1st Qu.:0
Median : 4.000 Median :164.0 Median : 66.0 Median : 15.00 Median :0
Mean : 6.009 Mean :163.2 Mean : 67.7 Mean : 20.17 Mean :0
3rd Qu.:11.000 3rd Qu.:194.0 3rd Qu.: 98.0 3rd Qu.: 29.00 3rd Qu.:0
Max. :12.000 Max. :352.0 Max. : 221.0 Max. :104.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.00000 Min. :28.48 Min. :-16.3292 Min. : 1.0
1st Qu.: 0.00000 1st Qu.:39.49 1st Qu.: -5.6156 1st Qu.: 44.0
Median : 0.00000 Median :41.38 Median : -3.1742 Median : 240.0
Mean : 0.07386 Mean :40.83 Mean : -2.8205 Mean : 347.5
3rd Qu.: 0.00000 3rd Qu.:42.45 3rd Qu.: 0.3664 3rd Qu.: 617.0
Max. :75.00000 Max. :43.57 Max. : 4.2156 Max. :1894.0
if (!empty_nodes) summary(df.cluster02)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : -8.0 Min. :-58.00 Min. :100.0 Min. :0
1st Qu.: 3.000 1st Qu.:119.0 1st Qu.: 57.00 1st Qu.:110.0 1st Qu.:0
Median :10.000 Median :140.0 Median : 78.00 Median :121.0 Median :0
Mean : 7.533 Mean :146.3 Mean : 79.49 Mean :132.5 Mean :0
3rd Qu.:11.000 3rd Qu.:170.0 3rd Qu.:102.00 3rd Qu.:141.8 3rd Qu.:0
Max. :12.000 Max. :350.0 Max. :223.00 Max. :422.0 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.0000 Min. :27.82 Min. :-17.889 Min. : 1.00
1st Qu.: 0.0000 1st Qu.:41.29 1st Qu.: -8.616 1st Qu.: 36.75
Median : 0.0000 Median :42.43 Median : -7.860 Median : 256.00
Mean : 0.6936 Mean :41.34 Mean : -5.770 Mean : 340.98
3rd Qu.: 0.0000 3rd Qu.:42.89 3rd Qu.: -2.039 3rd Qu.: 370.00
Max. :123.0000 Max. :43.57 Max. : 3.035 Max. :2400.00
if (!empty_nodes) summary(df.cluster03)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : 87 Min. : 64.0 Min. : 0.000 Min. :0
1st Qu.: 3.000 1st Qu.:212 1st Qu.:150.0 1st Qu.: 0.000 1st Qu.:0
Median : 6.000 Median :233 Median :168.0 Median : 1.000 Median :0
Mean : 6.486 Mean :234 Mean :169.5 Mean : 6.141 Mean :0
3rd Qu.: 9.000 3rd Qu.:259 3rd Qu.:196.0 3rd Qu.: 7.000 3rd Qu.:0
Max. :12.000 Max. :356 Max. :244.0 Max. :103.000 Max. :0
prof_nieve longitud latitud altitud
Min. :0.0000000 Min. :27.82 Min. :-17.89 Min. : 14.0
1st Qu.:0.0000000 1st Qu.:28.05 1st Qu.:-16.56 1st Qu.: 25.0
Median :0.0000000 Median :28.46 Median :-16.26 Median : 33.0
Mean :0.0003525 Mean :28.38 Mean :-15.95 Mean :130.3
3rd Qu.:0.0000000 3rd Qu.:28.48 3rd Qu.:-15.39 3rd Qu.: 64.0
Max. :2.0000000 Max. :28.95 Max. :-13.60 Max. :632.0
if (!empty_nodes) summary(df.cluster04)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-53.00 Min. :-121.000 Min. : 0.00
1st Qu.: 3.000 1st Qu.: 29.00 1st Qu.: -34.000 1st Qu.: 19.00
Median : 6.000 Median : 69.00 Median : 3.000 Median : 31.00
Mean : 6.507 Mean : 74.65 Mean : 6.685 Mean : 34.99
3rd Qu.:10.000 3rd Qu.:119.00 3rd Qu.: 47.250 3rd Qu.: 47.00
Max. :12.000 Max. :218.00 Max. : 128.000 Max. :126.00
nevada prof_nieve longitud latitud altitud
Min. :0 Min. : 0.000 Min. :40.78 Min. :-4.0103 Min. :1405
1st Qu.:0 1st Qu.: 0.000 1st Qu.:41.77 1st Qu.: 0.7292 1st Qu.:1894
Median :0 Median : 0.000 Median :42.47 Median : 0.9844 Median :2230
Mean :0 Mean : 3.539 Mean :42.06 Mean : 0.1067 Mean :2150
3rd Qu.:0 3rd Qu.: 0.000 3rd Qu.:42.64 3rd Qu.: 1.5242 3rd Qu.:2400
Max. :0 Max. :165.000 Max. :42.77 Max. : 2.4378 Max. :2535
if (!empty_nodes) summary(df.cluster05)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. :163.0 Min. : 42.0 Min. : 0.000 Min. :0
1st Qu.: 6.000 1st Qu.:245.0 1st Qu.:122.0 1st Qu.: 1.000 1st Qu.:0
Median : 7.000 Median :275.0 Median :152.0 Median : 5.000 Median :0
Mean : 7.361 Mean :274.8 Mean :151.6 Mean : 7.276 Mean :0
3rd Qu.: 9.000 3rd Qu.:303.0 3rd Qu.:181.0 3rd Qu.:12.000 3rd Qu.:0
Max. :12.000 Max. :403.0 Max. :254.0 Max. :44.000 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.00000 Min. :35.28 Min. :-8.6239 Min. : 1.0
1st Qu.: 0.00000 1st Qu.:38.28 1st Qu.:-4.1153 1st Qu.: 43.0
Median : 0.00000 Median :40.41 Median :-1.8631 Median : 247.0
Mean : 0.00245 Mean :39.88 Mean :-1.9231 Mean : 380.1
3rd Qu.: 0.00000 3rd Qu.:41.52 3rd Qu.: 0.5706 3rd Qu.: 667.0
Max. :35.00000 Max. :43.36 Max. : 4.2156 Max. :1894.0
if (!empty_nodes) summary(df.cluster06)
fecha_cnt tmax tmin precip nevada
Min. :2 Min. :71.00 Min. :-1 Min. :14.00 Min. :4.0
1st Qu.:2 1st Qu.:72.25 1st Qu.:-1 1st Qu.:15.25 1st Qu.:4.5
Median :2 Median :73.50 Median :-1 Median :16.50 Median :5.0
Mean :2 Mean :73.50 Mean :-1 Mean :16.50 Mean :5.0
3rd Qu.:2 3rd Qu.:74.75 3rd Qu.:-1 3rd Qu.:17.75 3rd Qu.:5.5
Max. :2 Max. :76.00 Max. :-1 Max. :19.00 Max. :6.0
prof_nieve longitud latitud altitud
Min. : 5 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.: 7 1st Qu.:40.78 1st Qu.:-2.846 1st Qu.:349.3
Median : 9 Median :41.08 Median :-2.242 Median :435.6
Mean : 9 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:11 3rd Qu.:41.37 3rd Qu.:-1.637 3rd Qu.:521.8
Max. :13 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster07)
fecha_cnt tmax tmin precip
Min. : 1.0 Min. : 69.00 Min. :-24.00 Min. : 3.00
1st Qu.: 2.0 1st Qu.: 86.25 1st Qu.:-12.50 1st Qu.: 6.00
Median : 7.0 Median : 92.00 Median : 6.50 Median :10.50
Mean : 6.8 Mean : 96.80 Mean : 7.90 Mean :11.40
3rd Qu.:12.0 3rd Qu.: 96.75 3rd Qu.: 29.25 3rd Qu.:14.75
Max. :12.0 Max. :140.00 Max. : 35.00 Max. :24.00
nevada prof_nieve longitud latitud altitud
Min. :1.00 Min. :0.0 Min. :40.48 Min. :-3.450 Min. :263.0
1st Qu.:1.25 1st Qu.:0.0 1st Qu.:40.48 1st Qu.:-3.450 1st Qu.:263.0
Median :2.00 Median :0.5 Median :41.08 Median :-2.242 Median :435.6
Mean :1.80 Mean :1.1 Mean :41.08 Mean :-2.242 Mean :435.6
3rd Qu.:2.00 3rd Qu.:1.0 3rd Qu.:41.67 3rd Qu.:-1.033 3rd Qu.:608.1
Max. :3.00 Max. :7.0 Max. :41.67 Max. :-1.033 Max. :608.1
if (!empty_nodes) summary(df.cluster08)
fecha_cnt tmax tmin precip
Min. : 1.000 Min. :-12.00 Min. :-72.00 Min. : 5.00
1st Qu.: 1.000 1st Qu.: 12.75 1st Qu.:-40.75 1st Qu.: 30.75
Median : 2.000 Median : 27.00 Median :-26.50 Median : 49.50
Mean : 3.062 Mean : 30.47 Mean :-26.97 Mean : 50.75
3rd Qu.: 3.000 3rd Qu.: 46.00 3rd Qu.:-16.50 3rd Qu.: 68.50
Max. :12.000 Max. : 99.00 Max. : 28.00 Max. :180.00
nevada prof_nieve longitud latitud altitud
Min. :0 Min. :175.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.:0 1st Qu.:218.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :0 Median :280.5 Median :40.78 Median :-4.01 Median :1894
Mean :0 Mean :339.0 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:0 3rd Qu.:438.0 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :0 Max. :709.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster09)
fecha_cnt tmax tmin precip nevada
Min. :1.000 Min. :-4.00 Min. :-51.00 Min. : 19.00 Min. :0
1st Qu.:2.000 1st Qu.: 4.50 1st Qu.:-43.50 1st Qu.: 45.00 1st Qu.:0
Median :2.500 Median :21.00 Median :-30.00 Median : 50.00 Median :0
Mean :2.375 Mean :18.88 Mean :-33.00 Mean : 58.25 Mean :0
3rd Qu.:3.000 3rd Qu.:35.25 3rd Qu.:-23.75 3rd Qu.: 63.75 3rd Qu.:0
Max. :3.000 Max. :36.00 Max. :-16.00 Max. :122.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 784.0 Min. :40.78 Min. :-4.01 Min. :1894
1st Qu.: 865.8 1st Qu.:40.78 1st Qu.:-4.01 1st Qu.:1894
Median :1045.0 Median :40.78 Median :-4.01 Median :1894
Mean :1131.1 Mean :40.78 Mean :-4.01 Mean :1894
3rd Qu.:1249.5 3rd Qu.:40.78 3rd Qu.:-4.01 3rd Qu.:1894
Max. :1834.0 Max. :40.78 Max. :-4.01 Max. :1894
if (!empty_nodes) summary(df.cluster10)
fecha_cnt tmax tmin precip nevada
Min. : 1.000 Min. : 13.0 Min. :-33.00 Min. : 0.00 Min. :0
1st Qu.: 4.000 1st Qu.: 94.0 1st Qu.: 23.00 1st Qu.: 0.00 1st Qu.:0
Median : 7.000 Median :129.0 Median : 51.00 Median : 3.00 Median :0
Mean : 6.585 Mean :138.4 Mean : 60.83 Mean : 10.29 Mean :0
3rd Qu.:10.000 3rd Qu.:183.0 3rd Qu.: 98.00 3rd Qu.: 13.00 3rd Qu.:0
Max. :12.000 Max. :253.0 Max. :159.00 Max. :114.00 Max. :0
prof_nieve longitud latitud altitud
Min. : 0.0000 Min. :28.31 Min. :-16.5 Min. :2371
1st Qu.: 0.0000 1st Qu.:28.31 1st Qu.:-16.5 1st Qu.:2371
Median : 0.0000 Median :28.31 Median :-16.5 Median :2371
Mean : 0.1766 Mean :28.31 Mean :-16.5 Mean :2371
3rd Qu.: 0.0000 3rd Qu.:28.31 3rd Qu.:-16.5 3rd Qu.:2371
Max. :46.0000 Max. :28.31 Max. :-16.5 Max. :2371
if (!empty_nodes) {
df.clusters.dim <- c(dim(df.cluster01)[1], dim(df.cluster02)[1], dim(df.cluster03)[1], dim(df.cluster04)[1], dim(df.cluster05)[1], dim(df.cluster06)[1], dim(df.cluster07)[1], dim(df.cluster08)[1], dim(df.cluster09)[1], dim(df.cluster10)[1])
barplot(df.clusters.dim,
names.arg = c("cluster01", "cluster02", "cluster03", "cluster04", "cluster05", "cluster06", "cluster07", "cluster08", "cluster09", "cluster10"),
col = "steelblue1")
}
if (!empty_nodes) mpr.hist(df.cluster01)
if (!empty_nodes) mpr.hist(df.cluster02)
if (!empty_nodes) mpr.hist(df.cluster03)
if (!empty_nodes) mpr.hist(df.cluster04)
if (!empty_nodes) mpr.hist(df.cluster05)
if (!empty_nodes) mpr.hist(df.cluster06)
if (!empty_nodes) mpr.hist(df.cluster07)
if (!empty_nodes) mpr.hist(df.cluster08)
if (!empty_nodes) mpr.hist(df.cluster09)
if (!empty_nodes) mpr.hist(df.cluster10)
if (!empty_nodes) mpr.boxplot(df.cluster01)
if (!empty_nodes) mpr.boxplot(df.cluster02)
if (!empty_nodes) mpr.boxplot(df.cluster03)
if (!empty_nodes) mpr.boxplot(df.cluster04)
if (!empty_nodes) mpr.boxplot(df.cluster05)
if (!empty_nodes) mpr.boxplot(df.cluster06)
if (!empty_nodes) mpr.boxplot(df.cluster07)
if (!empty_nodes) mpr.boxplot(df.cluster08)
if (!empty_nodes) mpr.boxplot(df.cluster09)
if (!empty_nodes) mpr.boxplot(df.cluster10)
# Agrupa por longitud y latitud para rellenar el mapa con menos datos.
if (!empty_nodes) {
df.cluster01.grouped <- mpr.group_by_geo(df.cluster01)
df.cluster02.grouped <- mpr.group_by_geo(df.cluster02)
df.cluster03.grouped <- mpr.group_by_geo(df.cluster03)
df.cluster04.grouped <- mpr.group_by_geo(df.cluster04)
df.cluster05.grouped <- mpr.group_by_geo(df.cluster05)
df.cluster06.grouped <- mpr.group_by_geo(df.cluster06)
df.cluster07.grouped <- mpr.group_by_geo(df.cluster07)
df.cluster08.grouped <- mpr.group_by_geo(df.cluster08)
df.cluster09.grouped <- mpr.group_by_geo(df.cluster09)
df.cluster10.grouped <- mpr.group_by_geo(df.cluster10)
}
if (!empty_nodes) mpr.draw_map(spain, df.cluster01.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster02.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster03.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster04.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster05.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster06.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster07.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster08.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster09.grouped)
if (!empty_nodes) mpr.draw_map(spain, df.cluster10.grouped)